The rapid expansion of e-commerce in Indonesia has resulted in a surge of unstructured online reviews, especially on platforms such as Shopee. These reviews offer valuable insights into customer satisfaction, product complaints, and purchasing behavior but remain largely underutilized due to their volume and informal language style. This study applies Support Vector Machine (SVM) with Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction to classify reviews of Android smartphones into positive, negative, and neutral categories. Using a dataset of 300 manually annotated reviews from Samsung, Xiaomi, and Oppo official stores, the model achieved an accuracy of 76.67% and demonstrated stable results through 5-fold cross-validation. The negative class showed the highest performance (F1 = 0.86), while the neutral class performed weakest (F1 = 0.62), reflecting challenges posed by mixed opinions and underrepresented samples. Compared with Naïve Bayes and Logistic Regression, the SVM model consistently outperformed both baselines, confirming its suitability for high-dimensional text data and informal Indonesian expressions. The findings highlight SVM’s potential to support automated sentiment monitoring in e-commerce, enabling businesses to identify emerging issues, improve customer service strategies, and leverage positive reviews for marketing. Future research should consider larger and more balanced datasets, techniques for handling imbalanced classes, and integration with deep learning models such as LSTM or BERT to improve performance and generalization.